MICbots: collecting large realistic datasets for speech and audio research using mobile robots
Identifieur interne : 000575 ( Main/Exploration ); précédent : 000574; suivant : 000576MICbots: collecting large realistic datasets for speech and audio research using mobile robots
Auteurs : Jonathan Le Roux [États-Unis] ; Emmanuel Vincent [France] ; John R. Hershey [États-Unis] ; Daniel P. W. Ellis [États-Unis]Source :
English descriptors
Abstract
Speech and audio signal processing research is a tale of data collection efforts and evaluation campaigns. Large benchmark datasets for automatic speech recognition (ASR) have been instrumental in the advancement of speech recognition technologies. However, when it comes to robust ASR, source separation, and localization, especially using microphone arrays, the perfect dataset is out of reach, and many different data collection efforts have each made different compromises between the conflicting factors in terms of realism, ground truth, and costs. Our goal here is to escape some of the most difficult trade-offs by proposing MICbots, a low-cost method of collecting large amounts of realistic data where annotations and ground truth are readily available. Our key idea is to use freely moving robots equiped with microphones and loudspeakers, playing recorded utterances from existing (already annotated) speech datasets. We give an overview of previous data collection efforts and the trade-offs they make, and describe the benefits of using our robot-based approach. We finally explain the use of this method to collect room impulse response measurement.
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Ellis</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-240679" status="INCOMING"><orgName>labROSA</orgName><desc><address><country key="US"></country></address></desc><listRelation><relation active="#struct-75524" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-75524" type="direct"><org type="institution" xml:id="struct-75524" status="VALID"><orgName>Columbia University [New York]</orgName><desc><address><addrLine>Columbia University in the City of New York 2960 Broadway New York, NY 10027-6902</addrLine><country key="US"></country></address><ref type="url">http://www.columbia.edu/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>États-Unis</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>mobile robots</term><term>resources</term><term>robust ASR</term><term>room acoustics</term><term>source separation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Speech and audio signal processing research is a tale of data collection efforts and evaluation campaigns. Large benchmark datasets for automatic speech recognition (ASR) have been instrumental in the advancement of speech recognition technologies. However, when it comes to robust ASR, source separation, and localization, especially using microphone arrays, the perfect dataset is out of reach, and many different data collection efforts have each made different compromises between the conflicting factors in terms of realism, ground truth, and costs. Our goal here is to escape some of the most difficult trade-offs by proposing MICbots, a low-cost method of collecting large amounts of realistic data where annotations and ground truth are readily available. Our key idea is to use freely moving robots equiped with microphones and loudspeakers, playing recorded utterances from existing (already annotated) speech datasets. We give an overview of previous data collection efforts and the trade-offs they make, and describe the benefits of using our robot-based approach. We finally explain the use of this method to collect room impulse response measurement.</div></front></TEI><affiliations><list><country><li>France</li><li>États-Unis</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="États-Unis"><noRegion><name sortKey="Le Roux, Jonathan" sort="Le Roux, Jonathan" uniqKey="Le Roux J" first="Jonathan" last="Le Roux">Jonathan Le Roux</name></noRegion><name sortKey="Ellis, Daniel P W" sort="Ellis, Daniel P W" uniqKey="Ellis D" first="Daniel P. W." last="Ellis">Daniel P. W. Ellis</name><name sortKey="Hershey, John R" sort="Hershey, John R" uniqKey="Hershey J" first="John R." last="Hershey">John R. Hershey</name></country><country name="France"><region name="Grand Est"><name sortKey="Vincent, Emmanuel" sort="Vincent, Emmanuel" uniqKey="Vincent E" first="Emmanuel" last="Vincent">Emmanuel Vincent</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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